Geospatial Data Analytics on AWS: Discover how to manage and analyze geospatial data in the cloud

✍ Scribed by Scott Bateman, Janahan Gnanachandran, Jeff DeMuth

• Publisher: Packt Publishing
• Tongue: English
• Leaves: 276
• Category: Library

✦ Synopsis

Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook.

Key Features

• Explore the architecture and different use cases to build and manage geospatial data lakes in AWS
• Discover how to leverage AWS purpose-built databases to store and analyze geospatial data
• Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud

Book Description

Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.

You'll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you'll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you'll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.

By the end of this book, you'll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.

What you will learn

• Discover how to optimize the cloud to store your geospatial data
• Explore management strategies for your data repository using AWS Single Sign-On and IAM
• Create effective SQL queries against your geospatial data using Athena
• Validate postal addresses using Amazon Location services
• Process structured and unstructured geospatial data efficiently using R
• Use Amazon SageMaker to enable machine learning features in your application
• Explore the free and subscription satellite imagery data available for use in your GIS

Who this book is for

If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.

Table of Contents

1. Introduction to Geospatial Data in the Cloud
2. Quality and Temporal Geospatial Data Concepts
3. Geospatial Data Lake Architecture
4. Using Geospatial Data with Amazon Redshift
5. Using Geospatial Data with Amazon Aurora PostgreSQL
6. Serverless Geospatial
7. Querying Geospatial Data with Amazon Athena
8. Geospatial Containers on AWS
9. Geospatial Data with Amazon EMR
10. Geospatial Data Analysis using Python on AWS Cloud9
11. Geospatial Data Analysis using SageMaker
12. Using Amazon QuickSight to Visualize Geospatial Data
13. Open Data on AWS
14. Leveraging OpenStreetMap on AWS
15. Map and Feature Services on AWS
16. Satellite Imagery on AWS

✦ Table of Contents

Cover
Title Page
Copyright and Credits
Contributors
Preface
Part 1: Introduction to the Geospatial Data Ecosystem
Introduction to Geospatial Data in the Cloud
Introduction to cloud computing and AWS
Storing geospatial data in the cloud
Building your geospatial data strategy
Preventing unauthorized access
The last mile in data consumption
Leveraging your AWS account team
Geospatial data management best practices
Data – it’s about both quantity and quality
People, processes, and technology are equally important
Cost management in the cloud
Right-sizing, simplified
The elephant in the server room
Bird’s-eye view on savings
Can’t we just add another server?
Additional savings at every desk
Summary
References
Quality and Temporal Geospatial Data Concepts
Quality impact on geospatial data
Transmission methods
Streaming data
Understanding file formats
Normalizing data
Considering temporal dimensions
Summary
References
Part 2: Geospatial Data Lakes using Modern Data Architecture
Geospatial Data Lake Architecture
Modern data architecture overview
The AWS modern data architecture pillars
Geospatial Data Lake
Designing a geospatial data lake using modern data architecture
Data collection and ingestion layer
Data storage layer
Data processing and transformation
Data analytics and insights
Data visualization and mapping
Summary
References
Using Geospatial Data with Amazon Redshift
What is Redshift?
Understanding Redshift partitioning
Redshift Spectrum
Redshift geohashing support
Redshift AQUA
Redshift geospatial support
Launching a Redshift cluster and running a geospatial query
Summary
References
Using Geospatial Data with Amazon Aurora PostgreSQL
Lab prerequisites
Setting up the database
Connecting to the database
Installing the PostGIS extension
Geospatial data loading
Queries and transformations
Architectural considerations
Summary
References
Serverless Options for Geospatial
What is serverless?
Serverless services
Object storage and serverless websites with S3
Geospatial applications and S3 web hosting
Serverless hosting security and performance considerations
Python with Lambda and API Gateway
Deploying your first serverless geospatial application
Summary
References
Querying Geospatial Data with Amazon Athena
Setting up and configuring Athena
Geospatial data formats
WKT
JSON-encoded geospatial data
Spatial query structure
Spatial functions
AWS service integration
Architectural considerations
Summary
References
Part 3: Analyzing and Visualizing Geospatial Data in AWS
Geospatial Containers on AWS
Understanding containers
Scaling containers
Container portability
GDAL
GeoServer
Updating containers
AWS services
Deployment options
Deploying containers
Summary
References
Using Geospatial Data with Amazon EMR
Introducing Hadoop
Introduction to EMR
Common Hadoop frameworks
EMRFS
Geospatial with EMR
Launching EMR
Summary
References
Geospatial Data Analysis Using R on AWS
Introduction to the R geospatial data analysis ecosystem
Setting up R and RStudio on EC2
RStudio on Amazon SageMaker
Analyzing and visualizing geospatial data using RStudio
Summary
References
Geospatial Machine Learning with SageMaker
AWS ML background
AWS service integration
Common libraries and algorithms
Introducing Geospatial ML with SageMaker
Deploying a SageMaker Geospatial example
First-time use steps
Geospatial data processing
Geospatial data visualization
Architectural considerations
Summary
References
Using Amazon QuickSight to Visualize Geospatial Data
Geospatial visualization background
Amazon QuickSight overview
Connecting to your data source
Configuring Athena
Configuring QuickSight
Visualization layout
Features and controls
Point maps
Filled maps
Putting it all together
Reports and collaboration
Summary
References
Part 4: Accessing Open Source and Commercial Platforms and Services
Open Data on AWS
What is open data?
Bird’s-eye view
Modern applications
The Registry of Open Data on AWS
Requester Pays model
Analyzing open data
Using your AWS account
Analyzing multiple data classes
Federated queries with Athena
Open Data on AWS benefits
Summary
References
Leveraging OpenStreetMapon AWS
What is OpenStreetMap?
OSM’s data structure
OSM benefits
Accessing OSM from AWS
Application – ski lift scout
The OSM community
Architectural considerations
Summary
References
Feature Servers and Map Servers on AWS
Types of servers and deployment options
Capabilities and cloud integrations
Deploying a container on AWS with ECR and EC2
Summary
Further reading
Satellite and Aerial Imagery on AWS
Imagery options
Sentinel
Landsat
NAIP
Architectural considerations
Demonstrating satellite imagery using AWS
Summary
References
Index
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